In the span of a few years, self-supervised learning has surpassed supervised methods as a way to pretrain neural networks [1]}, [2]}, [3]}, [4]}.
At the core of this success lies discriminative approaches that learn by differentiate between images [5]}, [6]} or clusters of images [7]}, [8]}.
Despite little assumptions made by these methods on the underlying factors of variations in the data, they produces features that are general enough to be re-used as they are in a variety of supervised tasks.
While this has been widely studied in the context of object-centric benchmarks, like ImageNet [9]} or COCO [10]}, we conjecture that this property is more general and could allow to recover any factor of variation in a given distribution of images.
In other words, this property can be leveraged to “discover” properties in uncurated datasets of images.
